Exploring novel systemic biomarker approaches in grass-pollen sublingual immunotherapy using omics

Abstract

Background: Sublingual allergen-specific immunotherapy (SLIT) intervention improves the control of grass pollen allergy by maintaining allergen tolerance after cessation. Despite its widespread use, little is known about systemic effects and kinetics associated to SLIT, as well as the influence of the patient sensitization phenotype (Mono- or Poly-sensitized). In this quest, omics sciences could help to gain new insights to understand SLIT effects.

Methods: 47 grass-pollen-allergic patients were enrolled in a double-blind, placebo-controlled, multicenter trial using GRAZAX® during 2 years. Immunological assays (sIgE, sIgG4, and ISAC) were carried out to 31 patients who finished the trial. Additionally, serum and PBMCs samples were analyzed by metabolomics and transcriptomics, respectively. Based on their sensitization level, 22 patients were allocated in Mono- or Poly-sensitized groups, excluding patients allergic to epithelia. Individuals were compared based on their treatment (Active/Placebo) and sensitization level (Mono/Poly).

Results: Kinetics of serological changes agreed with those previously described. At two years of SLIT, there are scarce systemic changes that could be associated to improvement in systemic inflammation. Poly-sensitized patients presented a higher inflammation at inclusion, while Mono-sensitized patients presented a reduced activity of mast cells and phagocytes as an effect of the treatment.

Conclusions: The most relevant systemic change detected after two years of SLIT was the desensitization of effector cells, which was only detected in Mono-sensitized patients. This change may be related to the clinical improvement, as previously reported, and, together with the other results, may explain why clinical effect is lost if SLIT is discontinued at this point.

Keywords: biomarkers; metabolomics; respiratory allergy; sublingual immunotherapy; transcriptomics.

Figure 1

I. A, Trial design. V: visit; GPS: grass pollen season; M: month(s); T: time (years). B, Final number of patients in each group according to treatment and sensitization. Mono: Mono‐sensitized; Poly: Poly‐sensitized; Poly‐Epi: Poly‐sensitized with epithelial allergy. C, Number of patients and samples used for each analysis. For further details, refer to Tables 1, Tables S1, and S2. II. Modulation of allergen‐specific Ig by grass‐tablet SLIT; Levels of sIgE (A.), sIgG4 (B.), and sIgE/sIgG4 ratio (C.) are shown as the log₂ of x‐fold change from baseline for the two main Phleum allergens, Phl p 1 + 5. 1st column graphs show data from all the patients in the study, 2s column graphs show data from Mono‐sensitized patients and 3rd column graphs show data from Poly‐sensitized patients. 4th column graphs show the trajectories for each patient. Continued lines represent the median and discontinued lines the quartiles in the Violin plots. *** P ≤ .001, ** P ≤ .01, * P ≤ .05

Figure 2

Significant signals from metabolomics between Active (n = 8) and Placebo (n = 14) groups at T2 were depicted on a heat map using hierarchical clustering of the samples (represented in columns) and metabolites (in rows). Red cells represent higher levels of the specific metabolite in that sample, whereas blue cells represent lower levels. Samples and metabolites are clustered according to their similarity. Mann‐Whitney U test with a Benjamini‐Hochberg correction was used to detect statistical significance (P < .05). Unknown features (metabolites without annotation) are represented by “Mass@Retention Time.” Numbers in parentheses refer to the metabolite Nº in Tables S5 and S6, where detailed information is available, including abbreviations

Figure 3

Significant signals in transcriptomics (A.) and metabolomics (B.) between Mono (n = 10) and Poly (n = 9 in transcriptomics, 12 in metabolomics) groups at T0 were depicted on heat maps using hierarchical clustering of the samples (represented in columns) and transcripts/metabolites (in rows). Red cells represent higher levels of the specific transcript/metabolite in that sample, whereas blue cells represent lower levels. Samples and transcripts/metabolites are clustered according to their similarity. Mann‐Whitney U test was used to detect statistical significance (P < .05). In B., unknown features (metabolites without annotation) are represented by “Mass@Retention Time.” Numbers in parentheses refer to the metabolite Nº in Tables S5 and S6, where detailed information is available, including abbreviations. C. IPA and GSEA significant transcriptomics results for “Mono vs Poly” comparison at T0

Figure 4

A. Significant signals from transcriptomics between Mono‐Active (n = 4) and Poly‐Active (n = 4) at T2 were depicted on a heat map using hierarchical clustering of the samples (represented in columns) and transcripts (in rows). Red cells represent higher levels of the specific transcript in that sample, whereas blue cells represent lower levels. Samples and transcripts are clustered according to their similarity. Mann‐Whitney U test was used to detect statistical significance (P < .05). B. IPA and GSEA significant results for “Mono‐Active vs Poly‐Active at T2” comparison

Figure 5

A. Significant signals from transcriptomics between Mono‐Active T2 (n = 4) and Mono‐Active T0 (n = 4) were depicted on a heat map using hierarchical clustering of the samples (represented in columns) and transcripts (in rows). Red cells represent higher levels of the specific transcript in that sample, whereas blue cells represent lower levels. Samples and transcripts are clustered according to their similarity. Mann‐Whitney U test was used to detect statistical significance (P < .05). B. IPA and GSEA significant results for “Mono‐Active T2 vs Mono‐Active T0” comparison